| Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs LC Chen, G Papandreou, I Kokkinos, K Murphy, AL Yuille IEEE transactions on pattern analysis and machine intelligence 40 (4), 834-848, 2017 | 25190* | 2017 |
| Machine learning, a probabilistic perspective C Robert CHANCE 27 (2), 62-63, 2014 | 17456* | 2014 |
| LabelMe: a database and web-based tool for image annotation BC Russell, A Torralba, KP Murphy, WT Freeman International journal of computer vision 77, 157-173, 2008 | 4767 | 2008 |
| Dynamic bayesian networks: representation, inference and learning KP Murphy University of California, Berkeley, 2002 | 4010 | 2002 |
| Speed/accuracy trade-offs for modern convolutional object detectors J Huang, V Rathod, C Sun, M Zhu, A Korattikara, A Fathi, I Fischer, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 3568 | 2017 |
| Progressive neural architecture search C Liu, B Zoph, M Neumann, J Shlens, W Hua, LJ Li, L Fei-Fei, A Yuille, ... Proceedings of the European conference on computer vision (ECCV), 19-34, 2018 | 2441 | 2018 |
| Loopy belief propagation for approximate inference: An empirical study K Murphy, Y Weiss, MI Jordan arXiv preprint arXiv:1301.6725, 2013 | 2370 | 2013 |
| Knowledge Vault: A Web-scale approach to probabilistic knowledge fusion XL Dong, K Murphy, E Gabrilovich, G Heitz, W Horn, N Lao, T Strohmann, ... KDD, 2014 | 2280 | 2014 |
| Rao-Blackwellised particle filtering for dynamic Bayesian networks K Murphy, S Russell Sequential Monte Carlo methods in practice, 499-515, 2001 | 2091 | 2001 |
| A review of relational machine learning for knowledge graphs M Nickel, K Murphy, V Tresp, E Gabrilovich Proceedings of the IEEE 104 (1), 11-33, 2015 | 2011 | 2015 |
| Deep variational information bottleneck AA Alemi, I Fischer, JV Dillon, K Murphy arXiv preprint arXiv:1612.00410, 2016 | 1942 | 2016 |
| The bayes net toolbox for matlab K Murphy Computing science and statistics 33, 2001 | 1741 | 2001 |
| Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification S Xie, C Sun, J Huang, Z Tu, K Murphy Proceedings of the European conference on computer vision (ECCV), 305-321, 2018 | 1648 | 2018 |
| Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation G Papandreou, LC Chen, KP Murphy, AL Yuille Proceedings of the IEEE international conference on computer vision, 1742-1750, 2015 | 1573 | 2015 |
| Videobert: A joint model for video and language representation learning C Sun, A Myers, C Vondrick, K Murphy, C Schmid Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1449 | 2019 |
| Probabilistic machine learning: an introduction KP Murphy MIT press, 2022 | 1421* | 2022 |
| Generation and comprehension of unambiguous object descriptions J Mao, J Huang, A Toshev, O Camburu, AL Yuille, K Murphy Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1394 | 2016 |
| Context-based vision system for place and object recognition Torralba Proceedings Ninth IEEE International Conference on Computer Vision, 273-280 …, 2003 | 1297 | 2003 |
| Towards accurate multi-person pose estimation in the wild G Papandreou, T Zhu, N Kanazawa, A Toshev, J Tompson, C Bregler, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1095 | 2017 |
| Naive bayes classifiers KP Murphy University of British Columbia 18 (60), 1-8, 2006 | 1033 | 2006 |